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Results for “conditional generative models”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 91 records · Page 5
All sourcesNASA NTRSDOE OSTI
NASA NTRS2020

Supporting Information for ”The 2019-2020 Australian Drought and Bushfires Altered the Partitioning of Hydrological Fluxes

The model configuration employs the modified International Geosphere Biosphere Programme (IGBP) MODIS 20 category landcover data, soil parameters derived from the International Soil Reference and Information Centre, and the Shuttle Radar Topography Mission based elevation, slope, and aspect data. Statistical downscaling approaches are used to transform the coarse resolution MERRA2 meteorological inputs to 1km. The input meteorological fields of air temperature, humidity, surface pressure, wind, downward shortwave radiation, and downward longwave radiation are downscaled to 1km by adjusting for terrain differences in elevation, slope, and aspect. The high resolution monthly precipitation climatology from WorldClim is used to spatially disaggregate input MERRA2 precipitation to 1km. The initial conditions for the model simulations are generated from a long spinup of NoahMP starting from year 2000. All model integrations and evaluations are conducted using the NASA Land Information System and the Land surface Verification Toolkit.

Sujay V Kumar↗
NASA NTRSDate not supplied

Bimodal SLD Ice Accretion on Swept NACA 0012 Airfoil Models

An ice shape database has been created to document ice accretions on a swept 15-inch chord NACA0012 model and a swept 36-inch chord NACA0012 airfoil model resulting from an exposure to a Supercooled Large Drop (SLD) icing cloud with a bimodal drop size distribution. The ice shapes created were documented with photographs, laser scanned surface measurements over a section of the model span, and measurement of the ice mass over the same section of each accretion. The icing conditions were based on a series of reference conditions that were previously used to generate ice shapes on these models. Drop distribution effects were evaluated by using the same IRT icing conditions except with either monomodal or bimodal drop distributions. Ice shapes resulting from the bimodal distribution as well as from equivalent monomodal drop size distributions were obtained and compared. Results indicate that the ice shapes and mass values from the monomodal and bimodal drop size distributions were the same within the range of repeatability for the conditions tested in this campaign.

Icing↗
NASA NTRSDate not supplied

Development of a Full-Scale Finite Element Model of the Fokker F28 Fellowship Aircraft and Crash Simulation Predictions

In June 2019, a full-scale crash test of a Fokker F28 Fellowship aircraft was conducted as part of a joint National Aeronautics and Space Administration/Federal Aviation Administration (NASA/FAA) project to investigate the performance of transport aircraft under realistic crash conditions. The test objectives were to provide data for assessment of transport aircraft crashworthiness under realistic impact conditions and to generate test data for model validation. The test article was loaded with transport aircraft seats in a 3+2 configuration. A total of 24 instrumented ATDs were placed in the seats and restrained. The test article weighed 33,306-lb. (15,107.3-kg) and, during the crash test, impacted a 2-ft. (0.61-m) high soil bed at 65.3-ft/s (19.9-m/s) forward and 31.8-ft/s (9.7-m/s) vertical velocity. The full-scale aircraft crash test was simulated using the commercial nonlinear explicit transient dynamic finite element code, LS-DYNA. This paper will provide a description of the test article and the crash test conditions, document the F28 full-scale model development, and present test-analysis comparisons. The test-analysis comparisons include inertial properties, kinematic responses, structural acceleration responses, and structural deformation and failure.

Karen E Jackson↗
NASA NTRS1991

Diurnal forcing of planetary atmospheres

A free convection parameterization has been introduced into the Mars Planetary Boundary Layer Model (MPBL). Previously, the model would fail to generate turbulence under conditions of zero wind shear, even when statically unstable. This in turn resulted in erroneous results at the equator, for example, when the lack of Coriolis forcing allowed zero wind conditions. The underlying cause of these failures was the level 2 second-order turbulence closure scheme which derived diffusivities as algebraic functions of the Richardson number (the ratio of static stability to wind shear). In the previous formulation, the diffusivities were scaled by the wind shear--a convenient parameter since it is non-negative. This was the drawback that all diffusivities are zero under conditions of zero shear (viz., the free convection case). The new scheme tests for the condition of zero shear in conjunction with static instability and recalculates the diffusivities using a static stability scaling. The results for a simulation of the equatorial boundary layer at autumnal equinox are presented. (Note that after some wind shear is generated, the model reverts to the traditional diffusivity calculation.)

Houben, Howard C.↗
DOE OSTI2025

A comparison of probabilistic generative frameworks for molecular simulations

Generative artificial intelligence is now a widely used tool in molecular science. Despite the popularity of probabilistic generative models, numerical experiments benchmarking their performance on molecular data are lacking. Here, in this work, we introduce and explain several classes of generative models, broadly sorted into two categories: flow-based models and diffusion models. We select three representative models: neural spline flows, conditional flow matching, and denoising diffusion probabilistic models, and examine their accuracy, computational cost, and generation speed across datasets with tunable dimensionality, complexity, and modal asymmetry. Our findings are varied, with no one framework being the best for all purposes. In a nutshell, (i) neural spline flows do best at capturing mode asymmetry present in low-dimensional data, (ii) conditional flow matching outperforms other models for high-dimensional data with low complexity, and (iii) denoising diffusion probabilistic models appear the best for low-dimensional data with high complexity. Our datasets include a Gaussian mixture model and the dihedral torsion angle distribution of the Aib9 peptide, generated via a molecular dynamics simulation. We hope our taxonomy of probabilistic generative frameworks and numerical results may guide model selection for a wide range of molecular tasks.

Artificial intelligence↗
NASA NTRSDate not supplied

Sensitivity Analysis of the Human Research Program’s Impact 1.0 Model

Sensitivity analysis estimates the relative contribution of the uncertainty in input values to the uncertainty of model outputs. Partial Rank Correlation Coefficient (PRCC) and Leave One Out (LOO) are methods of conducting sensitivity analysis on non-linear simulation models like the IMPACT Model. The PRCC method estimates the sensitivity using partial correlation of the ranks of the generated input values to each generated output value. The “partial” part is due to the adjustments made for the linear effects of all the other input values in the calculation of correlation between a particular input and each output. LOO removes a medical condition from test suite and calculates the change in the outcome. This is used to identify the influence of the condition input in the model The inputs to the sensitivity procedures include the number of occurrences of each of the one hundred plus IMPACT medical conditions generated over the simulations, and the IMPACT outputs from the MEDPRAT mathematical model. The outputs total task time lost (TTL), number of return to definitive care (RTDC), and number of loss of crew lives (LOCL). The IMPACT team will report the results of using PRCC and LOO on IMPACT 1.0. Tornado plots will assist in the visualization of the condition-related input sensitivities to each of the main outcomes. The outcomes of this sensitivity analysis will drive review focus by identifying conditions where changes in uncertainty, and input values could drive changes in overall model output uncertainty. These efforts are an integral part of the overall verification, validation, and credibility review of IMPACT 1.0.

Sensitivity Anaylsis↗
DOE OSTI2025

PSA 2025 Presentation: "Modeling and Sensitivity Analysis of a Generation IV Pebble Bed Reactor Using MELCOR 2.2"

Accompanying the advancement of reactor technologies is the need for computational modeling and simulation to predict their behavior under normal operating conditions and accident scenarios. New Generation IV reactor designs which employ non-conventional fuel have a particular need for modeling the behavior and release of radionuclides and other material from the fuel. In this work, MELCOR version 2.2, a system-level safety and accident scenario code developed by Sandia National Laboratories, was used to model a 200-MWth pebble bed modular reactor and calculate the inventories of circulating and deposited graphite, metal dust, and elemental components released from the fuel elements. A base case modeling the reactor under standard operating conditions was calculated using MELCOR and the inventories were extrapolated to 30 years of operation time using a logarithmic regression fit. A sensitivity analysis was also performed in which several key parameters for the base case model were modified to explore the effect of these changes on the inventories calculated by MELCOR. A set of transient scenario simulations for a depressurized loss of forced cooling (DLOFC) accident were also performed. The results of the sensitivity analysis and transient simulations are reported and discussed in relation to the modeling techniques used for this study.

22 - GENERAL STUDIES OF NUCLEAR REACTORS↗
NASA NTRS1977

Subsonic wind-tunnel performance

Analysis of conditional sampling averages for turbulent flows shows how conditional averages can be related to conventional averages incorporated into existing flow models. Technique allows vast quantities of data generated by conditional sampling to be analyzed in coherent way.

Eckert, W. T.↗
NASA NTRS2009

From Verified Models to Verifiable Code

Declarative specifications of digital systems often contain parts that can be automatically translated into executable code. Automated code generation may reduce or eliminate the kinds of errors typically introduced through manual code writing. For this approach to be effective, the generated code should be reasonably efficient and, more importantly, verifiable. This paper presents a prototype code generator for the Prototype Verification System (PVS) that translates a subset of PVS functional specifications into an intermediate language and subsequently to multiple target programming languages. Several case studies are presented to illustrate the tool's functionality. The generated code can be analyzed by software verification tools such as verification condition generators, static analyzers, and software model-checkers to increase the confidence that the generated code is correct.

Lensink, Leonard↗
NASA NTRSDate not supplied

Evaluating the Efficacy of Conditional Variational Autoencoders in Generating Synthetic Single Nuclei RNA-Seq Data for Space Biology Research

Astronauts are subject to unique stressors during spaceflight, leading to changes in their cellular function. However, neither astronauts nor model organisms respond the same to spaceflight, and research implicates a contribution of omics components in differential responses. Understanding how gene expression affects astronaut health is critical for the success of long-term space missions, prompting interest in developing personalized predictive models leveraging artificial intelligence (AI) and machine learning (ML) techniques. Developing such models requires extensive data, which is challenging to obtain and share. This study explores the use of conditional variational autoencoders (CVAEs) to synthetically generate single-nuclei RNA-seq (snRNA-seq) data. CVAEs build on standard variational autoencoders (VAEs) by conditioning data generation on covariates like sample identity and mission parameters, enhancing the relevance of generated data for specific contexts. For our work, we built two CVAEs with varying degrees of sparsity to optimize both interpretability and generative power. We train and validate models on existing snRNA-seq data collected from the brain tissue of mice subjected to spaceflight conditions and their ground control counterparts. We evaluate model performance using statistical tests and visualizations to compare synthetic data to real data. We aim to demonstrate that these prototype CVAE architectures could be used in future space biology work and that this is a method worth further exploring.

Sarah Golts↗
NASA NTRS1991

Effects of the Gulf Stream on ocean waves

In the present study a third-generation numerical wave model is used to study effects of a straight Gulf Stream ring on ocean waves in swell and storm conditions. The model accounts for all relevant processes of propagation, generation, and dissipation of the waves (including current effects) without imposing a priori restraints on the spectral development of the waves. The dominating mechanism affecting the waves appears to be current-induced refraction even though the short-crestedness of the incoming waves tends to mask its effects (also in swell conditions). Depending on wind and wave conditions, refraction may trap locally generated waves in the straight Gulf Stream or it may reflect wave energy back to the open ocean. In the Gulf Stream ring, refraction induces a considerable variation in significant wave height and short-crestedness, but it hardly affects the mean wave direction. In storm conditions the processes of generation and dissipation are considerably enhanced in countercurrent situations and reduced following-current situations.

Holthuijsen, L. H.↗
DOE OSTI2026

Applying Particle Swarm Optimization and Extended Kalman Filtering to Model Kaplan Generation Dynamics for Hydropower Systems

Variable renewable generation is increasing the need for hydropower plants to provide fast and flexible grid support, which places new demands on plant-level dynamic models used for monitoring, control, and operational decision-making. This need is especially important for hydroelectric systems, where turbine and generator dynamics are strongly coupled, nonlinear, and time-varying, making accurate real-time representation difficult. To address this problem, this paper develops a digital twin (DT) framework for a synchronous generator–Kaplan turbine system using an explicit separation of slow turbine dynamics and fast generator dynamics. The turbine subsystem is represented by a six-coefficient model, whose parameters are identified offline using particle swarm optimization, while the generator subsystem is updated online through an extended Kalman filter for real-time state and parameter estimation. These models are integrated within a closed-loop simulation that includes a proportional–integral–derivative–double-derivative governor and excitation system, allowing the DT to track plant behavior under realistic operating conditions. Unlike prior studies that treat turbine and generator modeling separately or rely mainly on simulated inputs, the proposed framework is validated using real operational data from a hydropower plant. Results show that the DT reproduces terminal voltage, active power, and reactive power with a normalized root mean square error of approximately 5%. This hybrid offline–online formulation constitutes the main contribution of the work, providing an adaptive and practically deployable DT for hydropower systems with direct relevance to control improvement, performance monitoring, and grid-support applications under high renewable penetration.

13 HYDRO ENERGY↗
NASA NTRS2019

An Innovative Feedback Controls Design Approach for Aero Engines

The paper describes a feedback controls design approach for a generic regional jet turbofan engine, which can be adapted to aero engines in general. To demonstrate this approach, linear models for control design are generated at different operating conditions from a full envelope nonlinear simulation created with the NASA Glenn Research Center-developed Toolbox for the Modeling and Analysis of Thermodynamic Systems. The primary objective is to design a single feedback controller that achieves good performance, without the need of developing scheduled control designs to cover the engine operating envelope. An additional objective is to progressively design more robust controllers that can perform under large variations in plant dynamics to also cover control for engine limits and potentially for some off nominal or even damaged conditions.

Kopasakis, George↗
NASA NTRS1987

Generation of liquid water on Mars through the melting of a dusty snowpack

An optical/thermal model for dusty snowpacks at temperate Martian latitudes is used to investigate the possibility of valley network formation by liquid water that was provided by snowmelts, assuming insolation absorption under clear-sky conditions. The mean-annual surface temperatures for snow and the atmospheric exchange terms of the surface energy balance are constrained by global climate model results. Under favorable conditions, liquid water is generated at atmospheric pressures as low as 30-100 mbar, provided that the substrate is composed of regolith; this condition is in keeping with the cratered terrain expected in an ancient Martian surface.

Clow, Gary D.↗
DOE OSTI2026

Lessons Learned from Ecosystem-Scale Experimental Field Studies (Workshop Report)

Efforts to understand and predict ecosystem responses to environmental change require long-term, large-scale, spatially representative experiments and observations that capture natural variability, test predictive models, and generate transferable knowledge. Such studies are indispensable for unraveling the complexities of terrestrial ecosystems and their responses to disturbances and evolving environmental conditions, while generating the data necessary for developing mechanistic models and predictive tools that inform decision-making processes. Having a rich history of designing and executing large-scale ecosystem experiments, the U.S. Department of Energy’s Environmental System Science program convened a workshop in January 2025 that brought together leaders in the field to distill critical lessons from decades of experience in large-scale experiments. The workshop aimed to (1) provide an ecosystem experiment primer for best practices, thus ensuring a high scientific return on investment for funding agencies, and (2) offer a robust framework for the design and management of future research initiatives. This report synthesizes insights and experiences from workshop participants and is structured to capture the entire research life cycle, from goal setting and design to operations, adaptive management, team dynamics, collaborations, and the often overlooked aspect of decommissioning. By synthesizing decision-making and lessons learned across diverse research approaches, the report aims to provide a template of essential factors to consider when designing successful long-term, large-scale ecosystem experiments.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Geometry-complete diffusion for 3D molecule generation and optimization

Abstract Generative deep learning methods have recently been proposed for generating 3D molecules using equivariant graph neural networks (GNNs) within a denoising diffusion framework. However, such methods are unable to learn important geometric properties of 3D molecules, as they adopt molecule-agnostic and non-geometric GNNs as their 3D graph denoising networks, which notably hinders their ability to generate valid large 3D molecules. In this work, we address these gaps by introducing the Geometry-Complete Diffusion Model (GCDM) for 3D molecule generation, which outperforms existing 3D molecular diffusion models by significant margins across conditional and unconditional settings for the QM9 dataset and the larger GEOM-Drugs dataset, respectively. Importantly, we demonstrate that GCDM’s generative denoising process enables the model to generate a significant proportion of valid and energetically-stable large molecules at the scale of GEOM-Drugs, whereas previous methods fail to do so with the features they learn. Additionally, we show that extensions of GCDM can not only effectively design 3D molecules for specific protein pockets but can be repurposed to consistently optimize the geometry and chemical composition of existing 3D molecules for molecular stability and property specificity, demonstrating new versatility of molecular diffusion models. Code and data are freely available on GitHub .

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS2011

Method of performing computational aeroelastic analyses

Computational aeroelastic analyses typically use a mathematical model for the structural modes of a flexible structure and a nonlinear aerodynamic model that can generate a plurality of unsteady aerodynamic responses based on the structural modes for conditions defining an aerodynamic condition of the flexible structure. In the present invention, a linear state-space model is generated using a single execution of the nonlinear aerodynamic model for all of the structural modes where a family of orthogonal functions is used as the inputs. Then, static and dynamic aeroelastic solutions are generated using computational interaction between the mathematical model and the linear state-space model for a plurality of periodic points in time.

Silva, Walter A.↗
NASA NTRS2003

Prebiotic Synthesis of Autocatalytic Products From Formaldehyde-Derived Sugars as the Carbon and Energy Source

Our research objective is to understand and model the chemical processes on the primitive Earth that generated the first autocatalytic molecules and microstructures involved in the origin of life. Our approach involves: (a) investigation of a model origin-of-life process named the Sugar Model that is based on the reaction of formaldehyde- derived sugars (trioses and tetroses) with ammonia, and (b) elucidation of the constraints imposed on the chemistry of the origin of life by the fixed energies and rates of C,H,O-organic reactions under mild aqueous conditions. Recently, we demonstrated that under mild aqueous conditions the Sugar Model process yields autocatalytic products, and generates organic micropherules (2-20 micron dia.) that exhibit budding, size uniformity, and chain formation. We also discovered that the sugar substrates of the Sugar Model are capable of reducing nitrite to ammonia under mild aqueous conditions. In addition studies done in collaboration with Sandra Pizzarrello (Arizona State University) revealed that chiral amino acids (including meteoritic isovaline) catalyze both the synthesis and specific handedness of chiral sugars. Our systematic survey of the energies and rates of reactions of C,H,O-organic substrates under mild aqueous conditions revealed several general principles (rules) that govern the direction and rate of organic reactions. These reactivity principles constrain the structure of chemical pathways used in the origin of life, and in modern and primitive metabolism.

Weber, Arthur L.↗